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Ethical AI Integration in Enterprise Resource Planning Systems: A Framework for Balancing Innovation and Responsibility in B2B Environments

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  • Varun Sridharan

    (Independent Researcher, USA)

Abstract

This article examines the ethical dimensions of artificial intelligence integration within Enterprise Resource Planning (ERP) systems, with particular focus on manufacturing, distribution, and food & beverage sectors. The article proposes a comprehensive framework for balancing innovation imperatives with responsible AI practices in business-to-business environments where trust and regulatory compliance are paramount. The article identifies key challenges and best practices across three critical domains: ethical governance of decision-making algorithms, data privacy and security frameworks, and accessibility measures that address the digital divide between large and small enterprises. The article reveals that organizations implementing structured approaches to algorithmic transparency, bias mitigation, and inclusive design not only reduce ethical risks but also gain significant competitive advantages through enhanced trust, improved partner relationships, and more resilient business ecosystems. The proposed Ethical AI Governance Framework for ERP offers a practical roadmap for organizations at various stages of AI maturity, emphasizing that ethical implementation should be viewed not as a compliance exercise but as a strategic business imperative creating sustainable value across supply chains. This article contributes both theoretical insights and actionable guidance for technology providers, implementing organizations and regulatory bodies navigating the complex ethical landscape of AI-enhanced enterprise systems.

Suggested Citation

  • Varun Sridharan, 2025. "Ethical AI Integration in Enterprise Resource Planning Systems: A Framework for Balancing Innovation and Responsibility in B2B Environments," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 7(5), pages 489-504, May.
  • Handle: RePEc:alk:jcsts0:v:7:y:2025:i:5:id:9836
    DOI: 10.32996/jcsts.2025.7.5.56
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